suppressPackageStartupMessages({
  library(tidyverse)
  library(tidyr)
  library(here)
  library(ggplot2)
  library(dplyr)
  library(readxl)
  library(devtools)
  library(plotly)
  library(base)
  library(rio)
  library(plyr)
})

From upside data: ITQ versus non ITQ and the size of dot is the catch

Note: Corbett’s ITQ data is only until 2012. Chris’s upside data is now updated until 2016 with the more recent RAM data that Dan had. I ran all the figure and regressions under two “scenerios”:

1. until 2012
2. past 2012. extracted information about which fisheries had ITQs in 2012 and applied it   to the same fisheries into the future. This doesn't take into account other fisheries that   get ITQs in place during that time, or if fisheries stop ITQs

“KOBE” plots with ITQs not updated past 2012

ITQ_projection <- readRDS("data/ITQ_projection.rds")

f_b_itq <- ITQ_projection %>%
  select("BvBmsy", "FvFmsy", "itq", "iq", "ivq", "turf", "Catch") %>%
  filter( itq != "NA", iq != "NA", ivq != "NA") %>%
  mutate(rightsbased = case_when(
    itq == TRUE | iq == TRUE | ivq == TRUE ~ "1",
    itq == FALSE & iq == FALSE & ivq == FALSE ~ "0"))

F_B_graph <- ggplot(data = f_b_itq, aes( x=BvBmsy, y=FvFmsy, colour= rightsbased, size = Catch ))+
  geom_point()+
  labs(x = "B/Bmsy", y= "F/Fsmy") +
  theme_minimal()+
  theme(legend.title=element_blank())+
  ylim(-1, 5)+
  xlim(-.3, 3)+
  geom_hline(aes(yintercept=1))+
  geom_vline(aes(xintercept=1))

#F_B_graph
ggplotly(F_B_graph)

“KOBE” plots with ITQs updated past 2012

ITQ_projection_updated <- readRDS("data/ITQ_projection_updated.rds")

f_b_itq_updated <- ITQ_projection_updated %>%
  select("BvBmsy", "FvFmsy", "itq", "iq", "ivq", "turf", "Catch") %>%
  filter( itq != "NA", iq != "NA", ivq != "NA") %>%
  mutate(rightsbased = case_when(
    itq == TRUE | iq == TRUE | ivq == TRUE ~ "1",
    itq == FALSE & iq == FALSE & ivq == FALSE ~ "0"))


F_B_graph_updated <- ggplot(data = f_b_itq_updated, aes( x=BvBmsy, y=FvFmsy, colour= rightsbased, size = Catch ))+
  geom_point()+
  labs(x = "B/Bmsy", y= "F/Fsmy") +
  theme_minimal()+
  theme(legend.title=element_blank())+
  ylim(-1, 5)+
  xlim(-.3, 3)+
  geom_hline(aes(yintercept=1))+
  geom_vline(aes(xintercept=1))

#F_B_graph
ggplotly(F_B_graph_updated)